- Inference Demo of "VisemeNet-tensorflow"
- VisemeNet is an audio-driven animator centric speech animation driving a JALI or standard FACS-based face-rigging from input audio.
- The original repo is outdated and difficult to setup the environment for testing the pretrained model. This code is to provide a super-clean inference module based on the original author's repo.
Colab Demo
- This code provides the simple and clean inference code without any needless ones
- It's compatible with TF 2.0 Version
- Tensorflow 2.x
- numpy
- scipy
- python_speech_features
import numpy as np
from inference import VisemeRegressor
pb_filepath = "./visemenet_frozen.pb"
wav_file_path = "./test_audio.wav"
out_txt_path = "./maya_viseme_outputs.txt"
viseme_regressor = VisemeRegressor(pb_filepath=pb_filepath)
viseme_outputs = viseme_regressor.predict_outputs(wav_file_path=wav_file_path)
np.savetxt(out_txt_path, viseme_outputs, '%.4f')
- Python 3.6.x using "pyenv"
- Tensorflow 1.1.0
- Setup the envs and packages
# Install Virtualenv using pyenv
pyenv install 3.6.5
pyenv virtualenv 3.6.5 visemenet-freeze
pyenv activate visemenet-freeze
# Install packages
pip install tensorflow==1.1.0
- Clone the repo
# Clone Visemenet repo and the pretrained model
git clone https://github.com/yzhou359/VisemeNet_tensorflow.git
curl -L https://www.dropbox.com/sh/7nbqgwv0zz8pbk9/AAAghy76GVYDLqPKdANcyDuba?dl=0 > pretrained_model.zip
unzip prtrained_model.zip -d VisemeNet_tensorflow/data/ckpt/pretrain_biwi/
- Freeze Graph and Save as pb
# Freeze Graph
python freeze_graph.py
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